[R] Wilks Lamba

[Sven Garbade]

Peter Dalgaard BSA wrote:
> 
> Sven Garbade <garbade at psy.uni-muenchen.de> writes:
> 
> > Hi all,
> >
> > I can't figure out how to compute Wilks Lambda in a one way repeated
> > measure design. My matrix looks like:
> >
> > > t2.m
> >   Blank   ECR   ENC   UEA   UED
> > 1 -0.15  0.14  0.16  0.09  0.14
> > 2  0.30  0.08  0.14  0.14  0.14
> > [...]
> >
> > where each row is a case and the columns are levels of one factor (named
> > trial):
> >
> > > t2.fit <- manova(t2.m ~ 1)
> > > summary(t2.fit, intercept=T, test="Wilks")
> >             Df   Wilks approx F num Df den Df Pr(>F)
> > (Intercept)  1 0.26869  1.63302      5      3 0.3642
> > Residuals    7
> >
> > ist this correct? I ask because SPSS gives me a different result:
> >
> > Effect: Trial
> > Wilks:    0.392
> > F:        1.554
> > Df:       4
> > error Df: 4
> > Pr:       0.340
> >
> > Thanks for any hints, Sven
> 
> Hmm. The ways of SPSS are sometimes mysterious, but the Df suggest
> that you're testing that the 5 variables all have mean zero, whereas
> SPSS might be testing whether they have the *same* mean. You can check
> that by looking at
> 
> t2.m2 <- t2.m[,-1] -  t2.m[,1]
> t2.fit <- manova(t2.m ~ 1)

Thanks, that is exactly what I was looking for. Indeed  I need to test
that the 5 variables all have mean zero. But I haven't found anything in
the SPPS doc about what SPSS did, so I got confused.

Thanks & regards, Sven
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